The semi-supervised switchboard transcription project
Amarnag Subramanya, Jeff Bilmes · 2009
In previous work, we proposed a new graph-based semisupervised learning (SSL) algorithm and showed that it outperforms other state-of-the-art SSL approaches for classifying documents and web-pages. Here we use a multi-threaded implementation in order to scale the algorithm to very large data sets. We treat the phonetically annotated portion of the Switchboard transcription project (STP) as labeled data and automatically annotate (at the phonetic level) the Switchboard I (SWB) training set and show that our proposed approach outperforms stateof-the-art SSL algorithms as well as a state-of-the-art strictly supervised classifier. As a result, we have STP-style annotations of the entire SWB-I training set which we refer to as semisupervised STP (S3TP).